Crop Productivity Analysis#
Data and Assumptions#
Data#
The following datasets are utilized in this analysis for calculating and mapping crop productivity over the past years:
Dynamic World Dataset:
Source: Dynamic World - Google and the World Resources Institute (WRI)
Description: The Dynamic World dataset provides a near real-time, high-resolution (10-meter) global land cover classification. It is derived from Sentinel-2 imagery and utilizes machine learning models to classify land cover into nine distinct classes, including water, trees, grass, crops, built areas, bare ground, shrubs, flooded vegetation, and snow/ice. The dataset offers data with minimal latency, enabling near-immediate analysis and decision-making.
Spatial Resolution: 10 meters.
Temporal Coverage: Data is available since mid-2015, updated continuously as Sentinel-2 imagery becomes available capturing near Real-time.
MODIS Dataset:
Source: NASA’s Moderate Resolution Imaging Spectroradiometer MODIS on Terra and Aqua satellites.
Description: The MODIS dataset provides a wide range of data products, including land surface temperature, vegetation indices, and land cover classifications. It is widely used for monitoring and modeling land surface processes.
Spatial Resolution: 250 meters.
Temporal Coverage: Data is available from 2000 to the present, with daily to 16-day composite products.
GDP and Agricultural Data:
Source: Official government statistics and international databases.
Description: This dataset includes regional GDP figures, agricultural output, export data, and crop production statistics. It is used to analyze the economic impact of agricultural productivity.
Spatial Resolution: Varies by dataset; typically available at national and subnational levels.
Temporal Coverage: Varies by dataset; typically available annually or quarterly.
Administrative Boundaries:
Source: MIMU (Myanmar Information Management Unit)
Description: This dataset provides the administrative boundaries for Myanmar at various levels (national, state/region, district, township). It is used for spatial aggregation and analysis of crop productivity and economic data.
Spatial Resolution: Varies by administrative level.
Assumptions and Methodological Notes#
This analysis is based on the following assumptions and methodological considerations:
Temporal Definitions and Aggregations#
Fiscal Year Definition: The first quarter (Q1) of each fiscal year starts in April and ends in March of the following year. This aligns with Myanmar’s fiscal calendar.
Quarterly Aggregations: Monthly data are aggregated into quarters (Q1: April-June, Q2: July-September, Q3: October-December, Q4: January-March).
Methodological Assumptions#
Spatial Aggregation: Regional GDP values are based on single-year reference data and are assumed to maintain consistent spatial distributions across all years in the analysis period.
EVI Processing:
EVI values are median-aggregated within administrative boundaries to reduce noise
Time series preprocessing follows TIMESAT methodology: outlier removal, interpolation, and Savitzky-Golay smoothing
Seasonality parameters (start of season, middle of season, end of season) are extracted using a 20% threshold of maximum EVI
Cropland Identification: Pixels are classified as cropland based on Dynamic World’s machine learning classification. Classification accuracy varies by region and land cover type, with potential confusion between cropland and similar land covers.
Cropland Area Analysis#
Cropland Statistics by Administrative Level 1#
The table below shows the cropland area statistics by Administrative Level 1 for the years 2020 and 2025, along with the percentage change and absolute percentage change between these years. Except for Chin State, all regions experienced a decrease in cropland area from 2020 to 2025.
| Name | PCODE | Crop Area (ha) in 2020 | Crop Area (ha) in 2025 | % Change in Crop Area (2020-2025) | Absolute % Change in Crop Area (2020-2025) | |
|---|---|---|---|---|---|---|
| 0 | Sagaing | MMR005 | 1,523,048 | 1,355,659 | -10.99% | 10.99% |
| 1 | Ayeyarwady | MMR017 | 1,284,046 | 1,185,667 | -7.66% | 7.66% |
| 2 | Mandalay | MMR010 | 994,493 | 919,394 | -7.55% | 7.55% |
| 3 | Magway | MMR009 | 911,786 | 832,402 | -8.71% | 8.71% |
| 4 | Bago (East) | MMR007 | 675,028 | 612,430 | -9.27% | 9.27% |
| 5 | Yangon | MMR013 | 465,210 | 469,040 | 0.82% | 0.82% |
| 6 | Bago (West) | MMR008 | 463,752 | 442,237 | -4.64% | 4.64% |
| 7 | Shan (South) | MMR014 | 365,328 | 300,314 | -17.80% | 17.80% |
| 8 | Mon | MMR011 | 168,289 | 178,099 | 5.83% | 5.83% |
| 9 | Kachin | MMR001 | 214,885 | 164,590 | -23.41% | 23.41% |
| 10 | Shan (North) | MMR015 | 216,955 | 154,755 | -28.67% | 28.67% |
| 11 | Rakhine | MMR012 | 147,233 | 142,034 | -3.53% | 3.53% |
| 12 | Nay Pyi Taw | MMR018 | 136,528 | 117,129 | -14.21% | 14.21% |
| 13 | Kayin | MMR003 | 104,489 | 82,406 | -21.13% | 21.13% |
| 14 | Shan (East) | MMR016 | 40,675 | 36,171 | -11.07% | 11.07% |
| 15 | Tanintharyi | MMR006 | 19,499 | 12,136 | -37.76% | 37.76% |
| 16 | Kayah | MMR002 | 36,523 | 10,243 | -71.96% | 71.96% |
| 17 | Chin | MMR004 | 8,095 | 10,230 | 26.38% | 26.38% |
The following chart illustrates the changes in cropland area across different Administrative Level 1 regions from 2016 to 2025.
Satellite-Derived vs. Official Harvested Area Comparison#
The table below compares the satellite-derived cropland area estimates with official harvested area statistics in 2023. Compared to the official harvested area, the cropland area derived from satellite data is significantly lower, with the highest difference observed in Tanintharyi region.
| Admin Level 1 | year | Actual Harvested Area (acres) | Satellite-derived Crop Area (acres) | Actual Rank | Satellite-derived Rank | Percent Difference (%) | |
|---|---|---|---|---|---|---|---|
| 30 | Sagaing | 2023 | 15,737,204 | 3,962,762 | 1 | 1 | -297.13% |
| 31 | Ayeyarwady | 2023 | 15,600,272 | 3,029,523 | 2 | 2 | -414.94% |
| 32 | Bago | 2023 | 13,694,134 | 2,708,850 | 3 | 3 | -405.53% |
| 33 | Mandalay | 2023 | 9,080,864 | 2,645,312 | 5 | 4 | -243.28% |
| 34 | Magway | 2023 | 9,900,750 | 2,461,605 | 4 | 5 | -302.21% |
| 35 | Shan | 2023 | 7,783,626 | 1,762,997 | 6 | 6 | -341.50% |
| 36 | Yangon | 2023 | 4,259,410 | 1,120,116 | 7 | 7 | -280.26% |
| 37 | Kachin | 2023 | 1,905,864 | 588,800 | 11 | 8 | -223.69% |
| 38 | Mon | 2023 | 2,896,838 | 371,377 | 8 | 9 | -680.03% |
| 39 | Nay Pyi Taw | 2023 | 937,660 | 346,262 | 13 | 10 | -170.80% |
| 40 | Rakhine | 2023 | 2,647,330 | 320,716 | 10 | 11 | -725.44% |
| 41 | Kayin | 2023 | 2,737,796 | 188,712 | 9 | 12 | -1350.78% |
| 42 | Kayah | 2023 | 445,018 | 65,761 | 14 | 13 | -576.72% |
| 43 | Tanintharyi | 2023 | 1,608,060 | 36,513 | 12 | 14 | -4304.06% |
| 44 | Chin | 2023 | 272,288 | 24,694 | 15 | 15 | -1002.64% |
In terms of ranking by cropland area, both official harvested area and satellite-derived estimates show similar trends across regions, especially for Sagaing, Ayeyarwady, and Bago regions. However, there are several mismatches in region with smaller cropland area.
Crop Seasonality#
Using this time series dataset of EVI images, we apply several pre-processing steps to extract critical phenological parameters: start of season (SOS), middle of season (MOS), end of season (EOS), length of season (LOS), etc. This workflow is heavily inspired by the TIMESAT software.
Pre-processing steps
Remove outliers from dataset on per-pixel basis using median method: outlier if median from a moving window < or > standard deviation of time-series times 2.
Interpolate missing values linearly
Smooth data on per-pixel basis (using Savitsky Golay filter, window length of 3, and polyorder of 1)
Phenology Process
We then extract crop seasonality metrics using the seasonal amplitude method from the phenolopy package.
The chart below shows the result of this process for a single crop pixel. The blue dots represent the raw EVI values, the black line represents the processed EVI values, and the dotted lines represent season parameters extracted for that pixel: start of season, peak of season, and end of season.
Based on the phenology process, we identified the seasonality to start in July and end in February with the peak being in October. This can vary with geographic region and crop type as well, however, that has not been taken into consideration in this version.
EVI Time Series Analysis#
National Level Analysis#
The following figure shows the median EVI in Myanmar from 2010 to 2025 during the crop growing season (June to February). In general, the median EVI increases from 2010 to 2025.
State/Region Level Analysis (Admin 1)#
The figure below shows the trends of median EVI from 2010-2025 on admin level 1.
The figure below shows a choropleth maps of EVI for each admin level 1 from 2010-2025
The figure below shows the percent change of EVI compared to previous year from 2010-2025 on admin level 1.
District Level Analysis (Admin 2)#
The figure below shows a choropleth maps of EVI for each admin level 2 from 2010-2025
EVI and Economic Indicators#
EVI and Overall GDP#
The figure below shows the relationship between national median EVI and overall GDP from 2010 to 2025, with a weak positive correlation.
EVI and Agricultural GDP#
The figure below shows quarterly EVI (lagged one quarter) and agricultural GDP from 2010 to early 2025. The near-identical cyclical patterns suggest agricultural GDP materializes approximately one quarter later.
The figure below shows annual agricultural GDP and EVI from 2010 to early 2025. While the EVI remains steady, agricultural GDP shows a declining trend since 2020, likely due to external factors such as political instability.
The figure below shows the relationship between agricultural GDP and EVI with various lags. There’s a strong positive correlation between EVI one-quarter lag and agricultural GDP.
The table below presents regression results for national median EVI and agricultural GDP. Lagged EVI (one quarter) is statistically significant across all specifications. In the full model, a 1% increase in EVI corresponds to a 1.12% increase in agricultural GDP, holding other variables constant.
| Dependent variable: gdp_log | ||||
| Current EVI only | Current + 1 Quarter Ago | Current + 2 Quarters Ago | Current + 1 Quarter Ago + Is Crop Season | |
| (1) | (2) | (3) | (4) | |
| EVI (log) | 1.701*** | 1.173*** | -0.009 | -0.396*** |
| (0.447) | (0.311) | (0.547) | (0.147) | |
| NTL Sum (log) | -0.937*** | -0.642*** | -0.540*** | |
| (0.220) | (0.238) | (0.086) | ||
| EVI 1 Quarter Ago (log) | 2.334*** | 2.540*** | 1.115*** | |
| (0.299) | (0.294) | (0.132) | ||
| EVI 2 Quarter Ago (log) | -1.557** | |||
| (0.607) | ||||
| Is Crop Season | 1.484*** | |||
| (0.086) | ||||
| Observations | 60 | 53 | 53 | 53 |
| R2 | 0.200 | 0.705 | 0.741 | 0.959 |
| Adjusted R2 | 0.186 | 0.687 | 0.719 | 0.956 |
| Residual Std. Error | 0.749 (df=58) | 0.466 (df=49) | 0.441 (df=48) | 0.175 (df=48) |
| F Statistic | 14.463*** (df=1; 58) | 39.098*** (df=3; 49) | 34.311*** (df=4; 48) | 283.827*** (df=4; 48) |
| Note: | *p<0.1; **p<0.05; ***p<0.01 | |||
State-level EVI and Agricultural GDP#
The table below summarizes cropland statistics aggregated at Administrative Level 1 (State/Region) for the year 2015-2016. The regional GDP values across all years in the analysis period are based on this single-year reference data and are assumed to maintain consistent spatial distributions.
Regional GDP values are based on single-year reference data and are assumed to maintain consistent spatial distributions across all years in the analysis period.
| adm1_name | Agriculture | |
|---|---|---|
| 0 | Ayeyarwady | 16.0% |
| 1 | Chin | 0.5% |
| 2 | Kachin | 2.1% |
| 3 | Kayah | 0.5% |
| 4 | Kayin | 2.4% |
| 5 | Magway | 14.1% |
| 6 | Mandalay | 8.3% |
| 7 | Mon | 4.4% |
| 8 | Nay Pyi Taw | 1.1% |
| 9 | Rakhine | 3.3% |
| 10 | Sagaing | 17.0% |
| 11 | Tanintharyi | 6.9% |
| 12 | Yangon | 4.9% |
| 13 | Bago (East) | 5.3% |
| 14 | Bago (West) | 5.3% |
| 15 | Shan (South) | 2.7% |
| 16 | Shan (East) | 2.7% |
| 17 | Shan (North) | 2.7% |
The figure below shows the relationship between state-level median EVI and agricultural GDP across several lags. The positive correlation between lagged EVI (one quarter) and agricultural GDP persists at the state level.
The chart shows the relationship between state-level median EVI and agricultural GDP across states/regions. Colors differentiate data for the crop season versus the non-crop season. The figure suggests that during crop season, all states shows a positive relationship between EVI and agricultural GDP.
The table presents regression results for state-level median EVI and agricultural GDP.
| Dependent variable: gdp_log | |||||
| Current EVI only | Current + NTL | Current + NTL + 1 Quarter Ago | Current + NTL + 1 Quarter Ago + Is Crop Season | All Variables + Region Fixed Effects | |
| (1) | (2) | (3) | (4) | (5) | |
| Intercept | 11.544*** | 11.028*** | 11.503*** | 6.824*** | 13.923*** |
| (0.176) | (0.466) | (0.466) | (0.405) | (0.174) | |
| EVI (log) | 0.096 | 0.125 | 0.027 | -1.055*** | -0.244*** |
| (0.137) | (0.149) | (0.148) | (0.122) | (0.036) | |
| EVI 1 Quarter Ago (log) | 0.837*** | -0.099 | 0.674*** | ||
| (0.148) | (0.120) | (0.035) | |||
| Is Crop Season | 2.119*** | 1.602*** | |||
| (0.084) | (0.024) | ||||
| NTL Sum (log) | 0.053 | 0.097** | 0.150*** | -0.175*** | |
| (0.045) | (0.045) | (0.035) | (0.016) | ||
| Observations | 1057 | 938 | 937 | 937 | 937 |
| R2 | 0.000 | 0.002 | 0.035 | 0.425 | 0.963 |
| Adjusted R2 | -0.000 | -0.000 | 0.032 | 0.423 | 0.962 |
| Residual Std. Error | 1.264 (df=1055) | 1.270 (df=935) | 1.250 (df=933) | 0.965 (df=932) | 0.248 (df=915) |
| F Statistic | 0.495 (df=1; 1055) | 0.871 (df=2; 935) | 11.275*** (df=3; 933) | 172.378*** (df=4; 932) | 1129.960*** (df=21; 915) |
| Note: | *p<0.1; **p<0.05; ***p<0.01 | ||||
EVI and Crop Exports#
The figure below shows the relationship between national median EVI and total exports by crop. Except for beans and pulses, all crops exhibit positive correlation with lagged EVI (one quarter). Some crops show stronger relationships at a two-quarter lag, suggesting crop-specific export materialization timings.
The table below shows the regression result for total vegetable exports with various model specifications. Lagged EVI (one quarter) is statistically significant in all models. In the full model, a 1% increase in EVI corresponds to a 0.45% increase in total vegetable exports, holding other variables constant.
| Dependent variable: total_vegetable_exports_log | ||||
| Current EVI only | Current + 1 Lag | Current + 2 Lags | All Variables | |
| (1) | (2) | (3) | (4) | |
| EVI (log) | -0.448*** | -0.480*** | 0.028 | 0.064 |
| (0.114) | (0.109) | (0.189) | (0.188) | |
| EVI 1 Quarter Ago (log) | 0.348*** | 0.323*** | 0.449*** | |
| (0.110) | (0.104) | (0.120) | ||
| EVI 2 Quarters Ago (log) | 0.658*** | 0.408* | ||
| (0.192) | (0.227) | |||
| Is Crop Season | -0.208** | |||
| (0.103) | ||||
| Observations | 168 | 165 | 162 | 162 |
| R2 | 0.085 | 0.152 | 0.229 | 0.249 |
| Adjusted R2 | 0.079 | 0.141 | 0.215 | 0.230 |
| Residual Std. Error | 0.389 (df=166) | 0.369 (df=162) | 0.346 (df=158) | 0.343 (df=157) |
| F Statistic | 15.376*** (df=1; 166) | 14.510*** (df=2; 162) | 15.669*** (df=3; 158) | 13.011*** (df=4; 157) |
| Note: | *p<0.1; **p<0.05; ***p<0.01 | |||
The table below shows the regression results of the relationship total vegetables, beans and pulses, corn, and rice exports with national median EVI. Every crop except beans and pulses shows a statistically significant positive relationship with EVI from 1 quarter ago. The beans and pulses export have a significant relationship with EVI from 2 quarters ago.
| Total Vegetables | Beans/Pulses | Corn | Rice | |
| (1) | (2) | (3) | (4) | |
| Intercept | 7.523*** | 5.800*** | 5.012*** | 6.662*** |
| (0.499) | (0.585) | (1.469) | (0.770) | |
| EVI (log) | 0.064 | -0.107 | -0.249 | 0.324 |
| (0.188) | (0.220) | (0.553) | (0.290) | |
| EVI 1 Quarter Ago (log) | 0.449*** | -0.226 | 0.679* | 0.862*** |
| (0.120) | (0.141) | (0.354) | (0.185) | |
| EVI 2 Quarters Ago (log) | 0.408* | 0.777*** | 0.443 | 0.232 |
| (0.227) | (0.266) | (0.668) | (0.350) | |
| Is Crop Season | -0.208** | -0.080 | -0.201 | -0.170 |
| (0.103) | (0.120) | (0.302) | (0.158) | |
| Observations | 162 | 162 | 162 | 162 |
| R2 | 0.249 | 0.310 | 0.079 | 0.147 |
| Adjusted R2 | 0.230 | 0.292 | 0.055 | 0.125 |
| Residual Std. Error | 0.343 (df=157) | 0.402 (df=157) | 1.010 (df=157) | 0.529 (df=157) |
| F Statistic | 13.011*** (df=4; 157) | 17.618*** (df=4; 157) | 3.351** (df=4; 157) | 6.751*** (df=4; 157) |
| Note: | *p<0.1; **p<0.05; ***p<0.01 | |||